class_docstr: Handler for `FixedLenFeature` values.
  
  `FixedLenFeature` values will be parsed as a scalar or an array of the
  corresponding dtype. In case the value is missing the default_value will
  be returned. If the default value is not present a ValueError will be raised.
class_docstr_tok: ["Handler","for","`","FixedLenFeature","`","values",".","`","FixedLenFeature","`","values","will","be","parsed","as","a","scalar","or","an","array","of","the","corresponding","dtype",".","In","case","the","value","is","missing","the","default_value","will","be","returned",".","If","the","default","value","is","not","present","a","ValueError","will","be","raised","."]
docstr: Encode the value of this feature into the CSV line.
docstr_tok: ["Encode","the","value","of","this","feature","into","the","CSV","line","."]
code: def encode_value(self, string_list, values):
      """Encode the value of this feature into the CSV line."""
  
      if self._rank == 0:
          flattened_values = [values]
      elif self._rank == 1:
          # Short-circuit the reshaping logic needed for rank > 1.
          flattened_values = values
      else:
          flattened_values = np.asarray(
              values, dtype=self._np_dtype
          ).reshape(-1)
  
      if len(flattened_values) != self._size:
          raise ValueError(
              'FixedLenFeature "{}" got wrong number of values. Expected {} but '
              "got {}".format(
                  self._name, self._size, len(flattened_values)
              )
          )
  
      if self._encoder:
          string_list[self._index] = self._encoder.encode_record(
              flattened_values
          )
      else:
          string_list[self._index] = _to_string(flattened_values[0])
  
file_path: transform/tensorflow_transform/coders/csv_coder.py
imports: import csv
  import io
  import numpy
  import tensorflow
  from tensorflow_transform.tf_metadata import schema_utils
label_desc: Use image node_id 3 for calling the _FixedLenFeatureHandler obj's underlying member method code with example usage: obj.encode_value(string_list, values) without return types